Analytics Engineer Intern

This is a 12-week internship during summer 2027.

You'll join the Analytics Engineering team within Platform, which builds and maintains the trusted data infrastructure that powers decision-making across Coinbase. As an Analytics Engineer Intern, you'll model complex data, build scalable pipelines, and deliver insights that help teams across the company unlock the full potential of our data. You will gain hands-on experience building production data systems at scale, mentorship from senior analytics engineers, and exposure to how data powers decision-making across a publicly traded crypto-native company.

What you'll do:

• Build and maintain dimensional data models that transform raw data into clean, reliable datasets used by analysts, data scientists, and product teams across the organization.
• Develop data pipelines using SQL, ensuring data quality, freshness, and documentation meet production standards.
• Partner with cross-functional stakeholders to understand their data needs and deliver well-structured datasets or dashboards that enable self-serve analytics.
• Leverage generative AI tools to streamline data development, modeling, and documentation workflows.
• Investigate and resolve data quality issues, implementing tests and monitors that catch problems before they reach downstream consumers.

Required Skills and Experience:

• Currently pursuing a degree in computer science, data science, statistics, economics, or a related quantitative field, or equivalent experience
• Demonstrated proficiency in SQL through coursework, projects, or prior internship experience, with the ability to write complex queries involving joins, window functions, and aggregations.
• Familiarity with at least one programming language (Python preferred)
• Ability to communicate technical concepts clearly to both technical and non-technical audiences, demonstrated through project presentations, dashboards, documentation, or collaborative work.
• Utilizes generative AI responsibly, maintaining human oversight to deliver business-ready outputs and drive measurable improvements in workflow efficiency, cost, and quality.

 

Req ID: P78159

#LI-Hybrid